Implementation and practice of an integrated process to recover copper from low grade ore at Zijinshan mine

Chen, Jinghe; Guo, Xian Jian; Li, Hongxu

doi:10.1016/j.hydromet.2020.105394

Cited by 20 publications

(6 citation statements)

References 8 publications

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“…CNN has several convolution layers as it calculates convolution multiple times. Images with emphasized characteristics pass on to the next convolution layer and, as the layers go deeper, the images are emphasized more [22]. The spectral data for learning are 1-D data; however, we considered these data as pseudo 2-D data and input them to the CNN.…”

Section: Technicsmentioning

confidence: 99%

“…Secondly, this is because the grain size can be controlled by discriminating the grain size in the process, which makes it possible to improve the efficiency of mineral processing. At an actual operation site, there are only a few types of ores to be sorted [1,3,4,22,23]. Therefore, it was assumed that the classification performance is sufficient for only a few types of minerals to be classified at an operation site.…”

Section: Introductionmentioning

confidence: 99%

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Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing

et al. 2020

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In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%.

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Section: Technicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing

et al. 2020

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“…The typical microorganisms involved in bioleaching are mainly from the genera Acidithiobacillus, Leptospirillum, Acidiphilium, Sulfobacillus, Ferroplasma, Sulfolobus, Metallosphaera, and Acidianus (Rawlings, 2002;Yin et al, 2020). Industrial bioleaching mostly is undertaken by percolation in heaps where the abundant microorganisms are usually Acidithiobacillus, Leptospirillum, and Sulfobacillus (Acosta et al, 2014;Chen et al, 2020). For heap bioleaching, each heap surface is sprayed with basal salt solution, and air is injected at the bottom to enhance the microbial growth.…”

Section: Introductionmentioning

confidence: 99%

Bioleaching of Chalcopyrite Waste Rock in the Presence of the Copper Solvent Extractant LIX984N

Liu

Cao

et al. 2022

Front. Microbiol.

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Heap bioleaching, the solubilization of metal ions from metal sulfides by microbial oxidation, is often combined with solvent extraction (SX) and electrowinning to recover, e.g., copper from low-grade ores. After extraction, the leaching solution is recycled, but the entrained organic solvents may be toxic to the microorganisms. Here Acidithiobacillus ferrooxidans, Leptospirillum ferrooxidans, and Sulfobacillus thermosulfidooxidans were selected to perform bioleaching of chalcopyrite waste rock in the presence of the SX reagent (2.5% v/v LIX984N in kerosene). Possibly inhibitory effects have been evaluated by copper extraction, bacterial activity, number of actively Fe(II)-oxidizing cells, and biofilm formation. Microcalorimetry, most probable number determination, and atomic force microscopy combined with epifluorescence microscopy were applied. The results show that 100 and 300 mg/L SX reagent could hardly inhibit At. ferrooxidans from oxidizing Fe2+, but they seriously interfered with the biofilm formation and the oxidization of sulfur, thereby hindering bioleaching. L. ferrooxidans was sensitive to 50 mg/L SX reagent, which inhibited its bioleaching completely. Sb. thermosulfidooxidans showed different metabolic preferences, if the concentration of the SX reagent differed. With 10 mg/L LIX984N Sb. thermosulfidooxidans preferred to oxidize Fe2+ and extracted the same amount of copper as the assay without LIX984N. With 50 mg/L extractant the bioleaching stopped, since Sb. thermosulfidooxidans preferred to oxidize reduced inorganic sulfur compounds.

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“…Preparing the heap irrigation solution by adding sulfuric acid and ferric sulfate is costly [8], therefor their minimization, especially in the beginning of heap irrigation, is important to run a heap leaching plant. Luckily, metal sulfides such as pyrite, are a source of sulfuric acid and ferric ion, commonly found in copper sulfide deposits [9,10]. Pyrite oxidation provides acid and iron for the heap leaching.…”

Section: Introductionmentioning

confidence: 99%

“…Actually, acid consumption by gangue minerals excesses the acid generating processes for most copper heap bioleaching plants. Only few heap bioleaching plants, such as Zijinshan copper mine accumulated enough acid and iron in cycling solution during leaching under the excessive pyrite oxidation [9,12]. So for most heap bioleaching plants, accelerating of the pyrite oxidation can reduce the cost of sulfuric acid addition to the heap irrigation solution.…”

Section: Introductionmentioning

confidence: 99%

Industrial Heap Bioleaching of Copper Sulfide Ore Started with Only Water Irrigation

et al. 2021

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Sulfuric acid solution containing ferric iron is the extractant for industrial heap bioleaching of copper sulfides. To start a heap bioleaching plant, sulfuric acid is usually added to the irrigation solution to maintain adequate acidity (pH 1.0–2.0) for copper dissolution. An industrial practice of heap bioleaching of secondary copper sulfide ore that began with only water irrigation without the addition of sulfuric acid was successfully implemented and introduced in this manuscript. The mineral composition and their behavior related to the production and consumption of sulfuric acid during the bioleaching in heaps was analyzed. This indicated the possibility of self-generating of sulfuric acid in heaps without exogenous addition. After proving by batches of laboratory tests, industrial measures were implemented to promote the sulfide mineral oxidation in heaps throughout the acidifying stages, from a pH of 7.0 to 1.0, thus sulfuric acid and iron was produced especially by pyrite oxidation. After acidifying of the heaps, adapted microbial consortium was inoculated and established in a leaching system. The launch of the bioleaching heap and finally the production expansion were realized without the addition of sulfuric acid, showing great efficiency under low operation costs.

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Implementation and practice of an integrated process to recover copper from low grade ore at Zijinshan mine

Cited by 20 publications

References 8 publications

Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing

Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing

Bioleaching of Chalcopyrite Waste Rock in the Presence of the Copper Solvent Extractant LIX984N

Industrial Heap Bioleaching of Copper Sulfide Ore Started with Only Water Irrigation

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